Escalator monitoring system, method, sound data collection device and fixture therefor

ABSTRACT

An escalator monitoring system includes a data collection device disposed near parts of an escalator that need to be monitored for collecting data of the parts; a data transmittal device used to transmit data relevant to safe operation of the escalator; a local data processing device or cloud processors used to receive the data relevant to the safe operation of the escalator, to compare it against a threshold value stored therein and derived from the parts in normal operating conditions, and to respond to comparison result. The data collection device is a people traffic sensor for detecting the people traffic entering the escalator in a unit time, and an power sensor for detecting the power consumption in a unit time, a data transferring unit transfers the people traffic detected in a unit time and the power consumption detected in a unit time to the local data processing device or the cloud processors, the local data processing device or the cloud processors determines the running condition of the escalator according to the relation between the people traffic in a unit time and the power consumption in a unit time to determine whether or not an alarm signal needs to be sent.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. application Ser. No. 16/769,772filed on Jun. 4, 2020, which is the National Phase of PCT InternationalApplication No. PCT/CN2017/119810, filed on Dec. 29, 2017, which claimsthe priority benefit under 35 U.S.C. 119(a) to Chinese PatentApplication Nos. 201711498108.8, 201711498149.7, and 201711479430.6, allfiled in China on Dec. 29, 2017, and all of which are hereby expresslyincorporated by reference into the present application.

FIELD OF THE INVENTION

The present invention relates to an escalator monitoring system, anescalator monitoring method, a sound data collection device and afixture used therefor, and particularly, to an escalator monitoringsystem or an escalator monitoring method that uses data collected by adata collection device to analyze the operating condition of theescalator, a data collection device and a fixture used therefor.

BACKGROUND OF THE INVENTION

Escalators are widely used in a variety of occasions, including shoppingcenters, office buildings, public facilities, and in other indoor oroutdoor environments. Its safe and steady operation requires monitoringoperating conditions of relevant parts, and those operating conditionsdepend heavily on the occasion in which the escalator is running, thetemperature, humidity, the amount of dust surrounding it, as well as thepassenger traffic thereon and the frequency of use. Thus, everyescalator is in different condition and need frequent onsite inspectionand maintenance of those relevant parts for proper operation. Thisinvolves a great deal of work and makes it difficult for escalatormanufactures and users to keep track of its operating condition in anorganized fashion.

To solve this issue, an escalator monitoring system need to be improvedin such a way that it allows remote monitoring and produces raw data inconnection with the parts relevant to steady operation of the escalator.In addition, the system can process and analyze the raw data, promptlydetermine the operating condition of the escalator, warn of and preempta possible failure independent of human interference, and provide to themanufacture and the user of the escalator data generated during the itsoperation for reference.

SUMMARY OF THE INVENTION

A purpose of the present invention is to provide an escalator monitoringsystem that meets the needs described above, a data collection device,and a fixture used for the monitoring system, and an escalatormonitoring method.

According to a first aspect of the present invention, an escalatormonitoring system is provided, comprising a data collection devicedisposed near parts of an escalator that need to be monitored forcollecting data of the parts; a data transmittal device used to transmitdata relevant to safe operation of the escalator, a cloud processor usedto receive the data relevant to the safe operation of the escalator, tocompare it against a threshold value stored therein and derived from theparts in normal operating conditions, and to respond to comparisonresult.

Preferably the data collection device is a sound data collection device,a temperature collection device, a power collection device, or avibration collection device.

Preferably the escalator monitoring system further comprises a localdata processing device, the data relevant to the safe operation of theescalator is data processed by the local data processing device.

Preferably the local data processing device calculates special featurevalue of the data as the data relevant to the safe operation of theescalator.

Preferably if the comparison result is greater than the threshold value,the local data processing device sends out alarm signals regarding thesafe operation of the escalator or sends the comparison result to thecloud processor.

Preferably the cloud processor receives, processes, and compares thedata relevant to the safe operation of the escalator, which is the datacollected by the data collection device, against the threshold valuestored in the cloud processor and derived from the parts in normaloperating conditions.

Preferably the cloud processor calculates the special feature value ofthe data as the data relevant to the safe operation of the escalator,and compares it against the threshold value stored therein and derivedfrom the parts in normal operating conditions.

Preferably if the comparison result is greater than the threshold value,the cloud processor sends out alarm signals regarding the safe operationof the escalator and triggers a customer service system or directlycommunicates with maintenance personnel to conduct lubricationmaintenance.

Preferably the cloud processor includes an escalator database, historyoperation data of the escalator, the specification of the escalator, andits maintenance history stored therein.

Preferably the cloud processor compares the processed data and thethreshold value via means such as statistics, analytics, artificialintelligence, and machine learning.

Preferably the cloud processor saves the data of the parts relevant tothe safe operation of the escalator.

Preferably if the comparison result is greater than the threshold value,the cloud processor sends out alarm signals regarding the safe operationof the escalator and triggers a customer service system or directlycommunicates with maintenance personnel to conduct lubricationmaintenance.

Preferably the local data processing device is a micro-computer or a DSPchip.

Preferably the micro-computer sends the data to the cloud processor viaa wireless network.

Preferably the data collection device is a sound data collection device,and the local data processing device obtains through a band-pass filtersound data of the parts relevant to the safe operation of the escalatorwithin range of [F_(L), F_(H)], calculates root-mean-square (RMS) valueof the data, and sends the RMS value to the cloud processor where it iscompared against a sound data threshold value of the metal parts derivedwhen the metal parts operate in normal operating conditions and storedin the database of the cloud processor.

Preferably the data collection device is a sound data collection devicedisposed near the contact position of parts, that is, a drive and adrive chain, a drive chain and a drive chain sprocket or a step guidepad and a skirt panel, and used to calculate the special feature valueof the data and send the special feature value to the cloud processor tocompare it against a sound data threshold value of the metal partsderived when the parts operate in normal operating conditions and storedin the database of the cloud processor.

Preferably the data collection device is a sound data collection device,and the cloud processor receives the data sent from the data collectiondevice, obtains through a band-pass filter data of contacting partsrelevant to the safe operation of the escalator within the range of[F_(L), F_(H)], calculates the root-mean-square (RMS) value of the data,and compares the RMS value against a sound data threshold value of themetal parts derived when the parts operate in normal operatingconditions and stored in the database of the cloud processor.

Preferably the data collection device is a sound data collection device,and the cloud processor receives the sound data sent from the datacollection device, computes the special feature value of the data, andcompares the special feature value against a sound data threshold valueof the parts derived when the parts operate in normal operatingconditions and stored in the database of the cloud processor.

Preferably if the sound data comes from a set of escalators of the samelocation and possesses similar sound data patterns, the local dataprocessing device may choose only one of the sound data for processing.

Preferably the data collection device is a temperature sensor assemblyused to detect ambient temperature and temperature of back surface ofthe handrail of an escalator when it is turned off, in idling condition,or in full operating condition, the data transmittal device receivingthe ambient temperature and the handrail back surface temperature fromthe temperature sensor assembly and transferring the ambient temperatureand the handrail back surface temperature to the local data processingdevice or the cloud processor in a predetermined frequency, the localdata processing device or the cloud processor compares the ambienttemperature with the handrail back surface and obtains the temperaturedifference between the ambient temperature and the handrail backsurface, and wherein a predetermined temperature difference threshold isstored in the local data processing device or the cloud processor and ifthe temperature difference between the ambient temperature and thehandrail back surface temperature exceeds the predetermined temperaturedifference threshold, the local data processing device or the cloudprocessor will send an alarm signal.

Preferably the data collection device is a temperature sensor assemblyused to detect ambient temperature and the temperature of the backsurface of the handrail of an escalator in a predetermined period, thedata transmittal device sending the detected ambient temperature and thetemperature of the back surface of the handrail to the local dataprocessing device or the cloud processor, which compares between theambient temperature and the temperature of the back surface of handrail,and obtains a first mean value and a first variance of the temperaturedifference in the first predetermined period of time of them.

Preferably the temperature sensor assembly further collects ambienttemperatures and temperatures of the back surface of the handrail duringanother predetermined period of time, the data transmittal devicesending the detected ambient temperature and the temperature of the backsurface of the handrail to the local data processing device or the cloudprocessor, which compares between the ambient temperature and thetemperature of the back surface of handrail, and obtains a second meanvalue and a second variance of the temperature difference in anotherpredetermined period of time of them.

Preferably the local data processing device or the cloud processor isconfigured to send a first alarm signal when the difference between thefirst mean value and the second mean value is k times greater than thefirst variance, wherein k is an integer greater than 1 set in the localdata processing device or the cloud processor.

Preferably the local data processing device or the cloud processorfurther determines relation between the first variance and apredetermined first threshold and relation between the second varianceand a predetermined second threshold and if the first variance issmaller than the first threshold and the second variance is smaller thanthe second threshold, the local data processing device or the cloudprocessor will send a second alarm signal.

Preferably the data collection device is a people traffic sensor fordetecting the people traffic entering the escalator in a unit time, andan power sensor for detecting the power consumption in a unit time, adata transferring unit transfers the people traffic detected in a unittime and the power consumption detected in a unit time to the local dataprocessing device or the cloud processor, the local data processingdevice or the cloud processor determines the running condition of theescalator according to the relation between the people traffic in a unittime and the power consumption in a unit time to determine whether ornot an alarm signal needs to be sent.

Preferably the local processing device or the cloud processor calculatesa ratio between the power consumption in a unit time and the peopletraffic in a unit time and if the ratio changes abnormally, the localprocessing device or the cloud processor will send an alarm signal andif the ratio does not changes abnormally but has a tendency to increasein a predetermined time and is greater than a predetermined threshold,the local processing device or the cloud processor will send an alarmsignal.

Preferably the escalator is in a standby running condition, the powersensor detects an average power in a predetermined period and if theaverage power is not in a predetermined threshold range stored in thelocal data processing device or the cloud processor, the local dataprocessing device or the cloud processor will send an alarm signal.

Preferably the local data processing device or the cloud processor isfurther configured that in a period beyond a predetermined thresholdperiod, if the people traffic sensor detects that the people traffic is0 and the power sensor detects that the power consumption is not matchedto a set power consumption when the people traffic is 0, the local dataprocessing device or the cloud processor will send an alarm signal.

Preferably the local data processing device or the cloud processor isfurther configured that in a period beyond a predetermined thresholdperiod, if the people traffic sensor detects that the people traffic isnot 0 and the power sensor detects that the power consumption is astandby power output, the local data processing device or the cloudprocessor will send an alarm signal.

According to a second aspect, an escalator monitoring system isprovided, comprising a sound data collection device disposed parts of anescalator that need to be monitored for collecting data of the parts; alocal data processing device used to process sound data locally, totransmit the sound data to a cloud processor, or to respond to resultafter the sound data is locally processed.

According to a third aspect, an escalator monitoring system is provided,comprising a cloud processor used to receive sound data sent from aremote data collection device disposed near parts of an escalator thatneed to be monitored, to process the sound data, and to respond to theresult after the data is processed.

According to a fourth aspect, a sound data collection device includes acircuit board, one or more digital microphones, and a circuit boxenclosing the circuit board and the digital microphones, the circuit boxcomprising a top wall, a bottom wall, and side walls, and at least oneof the top and bottom walls having a sound picking hole therein.

Preferably a water-resistant membrane is arranged on the inner or outerside of the wall in which the sound picking hole is located in order toprevent water or humidity from entering into the digital microphones.

Preferably the digital microphones are arranged on the inner side of thewall in which the sound picking hole is located, below thewater-resistant membrane, or on the circuit board.

According to a fifth aspect, a fixture for the temperature collectiondevice for the escalator monitoring system is provided, wherein theinstallation position of the fixture is adjustable based on a drive modeof the handrail.

Preferably the fixture is installed on a C-profile component on theskirt panel of an escalator if the handrail is in a friction-wheel drivemode.

Preferably the fixture comprises a bracket and an adjustable plate, theadjustable plate being installed on the bracket, the bracket beinginstalled on the C-profile component of the skirt panel, the temperaturesensor assembly being installed on an end of the adjustable plate farfrom the C-profile component.

Preferably one of the adjustable plate and the bracket has a firstelongated hole extending along the length direction and in that theother has an opening, a connection device passing through the firstelongated hole and the opening to connect the adjustable plate and thebracket and to adjust distance between the temperature sensor assemblyand the C-profile component along the length direction based onalignment positions of the opening relative to the first elongated holealong the length direction.

Preferably the fixture is installed on a pillar of an escalator if thehandrail is in a newel-wheel drive mode.

Preferably the fixture comprises a bracket and an adjustable plate, theadjustable plate being installed on the bracket, the bracket beinginstalled on the pillar, the temperature sensor assembly being installedon an end of the adjustable plate far from the pillar.

Preferably one of the adjustable plate and the bracket has a secondelongated hole extending along the width direction and in that the otherhas a third elongated hole extending along the length direction, aconnection device passing through the second and third elongated holesto connect the adjustable plate and the bracket and to adjust distancebetween the temperature sensor assembly and the pillar along the lengthdirection and distance between the temperature sensor assembly and thehandrail along the width direction based on alignment positions of thesecond and third elongated holes.

According to a sixth aspect, an escalator is provided comprising anescalator monitoring system as above, a sound data collection device asabove and/or a fixture as above.

According to a seventh aspect, a monitoring method for an escalator isprovided, including the following steps:

-   -   using a temperature sensor assembly to detect a handrail back        surface temperature when the escalator is in at least one of a        closed down condition, a standby speed running condition and a        full speed running condition and ambient temperature;    -   using a data transferring unit to receive the ambient        temperature and the handrail back surface temperature from the        temperature sensor assembly and transfer the ambient temperature        and the handrail back surface temperature to a local data        processing device or a cloud processor in a predetermined        frequency;    -   wherein the local data processing device or the cloud processor        compares the ambient temperature with the handrail back surface        and obtains the temperature difference between the ambient        temperature and the handrail back surface, and wherein a        predetermined temperature difference threshold is stored in the        local data processing device or the cloud processor and if the        temperature difference between the ambient temperature and the        handrail back surface temperature exceeds the predetermined        temperature difference threshold, the local data processing        device or the cloud processor will send an alarm signal.

Preferably the method further comprises: using the local data processingdevice or the cloud processor to capture the handrail back surfacetemperature and the ambient temperature detected by the temperaturesensor assembly in a predetermined period and compare the ambienttemperature and the handrail back surface temperature, resulting a firstmean value and a first variance of the temperature difference betweenthe ambient temperature and the handrail back surface temperature in thepredetermined period.

Preferably the method further comprises: using the local data processingdevice or the cloud processor to capture the handrail back surfacetemperature and the ambient temperature detected by the temperaturesensor assembly in another predetermined period and compare the ambienttemperature and the handrail back surface temperature, resulting asecond mean value and a second variance of the temperature differencebetween the ambient temperature and the handrail back surfacetemperature in the another predetermined period.

Preferably the local data processing device or the cloud processor isconfigured that the local data processing device or the cloud processorsends a first alarm signal when the difference between the second meanvalue and the first mean value is greater than k times the firstvariance, wherein k, set in the local data processing device or thecloud processor, is an integer greater than 1.

Preferably the local data processing device or the cloud processorfurther determines the relation between the first variance and apredetermined first threshold and the relation between the secondvariance and a predetermined second threshold and if the first varianceis smaller than the first threshold and the second variance is smallerthan the second threshold, the local data processing device or the cloudprocessor will send a second alarm signal.

According to an eighth aspect, a monitoring method for an escalator isprovided, comprising the following steps:

-   -   using a people traffic sensor to detect people traffic entering        into the escalator in a unit time; using an power sensor to        detect the power consumption in a unit time; using a data        transferring unit to transfer the people traffic detected in a        unit time and the power consumption detected in a unit time to a        local data processing device and a cloud processor, wherein the        server determines the running condition of the escalator        according to the relation between the people traffic in a unit        time and the power consumption in a unit time to determine        whether or not an alarm signal needs to be sent.

Preferably the local data processing device or the cloud processorcalculates a ratio between the power consumption in a unit time and thepeople traffic in a unit time and if the ratio changes abnormally, thelocal data processing device or the cloud processor will send an alarmsignal and if the ratio does not changes abnormally but has a tendencyto increase in a predetermined time and is greater than a predeterminedthreshold, the local data processing device or the cloud processor willsend an alarm signal.

Preferably in the case that the escalator is in a standby runningcondition, the power sensor detects an average power in a predeterminedperiod and if the average power is not in a predetermined thresholdrange stored in the local data processing device or the cloud processor,the local data processing device or the cloud processor will send analarm signal.

Preferably the local data processing device or the cloud processor isfurther configured that in a period beyond a predetermined thresholdperiod, if the people traffic sensor detects that the people traffic is0 and the power sensor detects that the power consumption is not matchedto a set power consumption when the people traffic is 0, the local dataprocessing device or the cloud processor will send an alarm signal.

Preferably the local data processing device or the cloud processor isfurther configured that in a period beyond a predetermined thresholdperiod, if the people traffic sensor detects that the people traffic isnot 0 and the power sensor detects that the power consumption is astandby power output, the local data processing device or the cloudprocessor will send an alarm signal.

An escalator monitoring system or an escalator monitoring methodaccording to the present invention can collect data of an escalator andanalyze it in any environment, either indoor or outdoor or quiet ornoisy, independent of subjective human judgment. It also predictspossible failure without the need to stop the operation of theescalator, saves maintenance time and reduces associated cost, improvingits safety and ride comfort.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart with respect to the escalator monitoring systemin accordance with the first embodiment of the present invention.

FIG. 2 is a Spectrogram function spectrograph that exemplarilyillustrates the process of denoising low-frequency and high frequencynoises with the band-pass filter.

FIG. 3 illustrates the comparison between the calculated RMS value ofthe sound data and the threshold value in the first embodiment.

FIG. 4 is a flow chart with respect to the escalator monitoring systemin accordance with the second embodiment of the present invention.

FIG. 5 shows sound data patterns of failures of different types.

FIG. 6 is an illustration of the analysis flow of the classifier 8.

FIG. 7 is a flow chart with respect to the escalator monitoring systemin accordance with the third embodiment of the present invention.

FIG. 8 is a flow chart with respect to the escalator monitoring systemin accordance with the fourth embodiment of the present invention.

FIG. 9 is a flow chart with respect to the escalator monitoring systemin accordance with the fifth embodiment of the present invention.

FIG. 10 is a flow chart with respect to the escalator monitoring systemin accordance with the sixth embodiment of the present invention.

FIG. 11 is the first embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention.

FIG. 12 is the second embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention.

FIG. 13 is the third embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention.

FIG. 14 is the fourth embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention.

FIG. 15 is a block diagram with respect to the escalator monitoringsystem in accordance with the seventh embodiment of the presentinvention.

FIG. 16 shows friction-wheel handrail drive mode.

FIG. 17 shows newel-wheel handrail drive mode.

FIG. 18 shows a fixture installed on the C-profile component of theskirt panel under the friction-wheel handrail drive mode.

FIG. 19 is an enlarged view of A of FIG. 18 .

FIG. 20 is an illustration of the fixture of FIG. 18 .

FIG. 21 illustrates a temperature sensor assembly fixed on an escalatorby the fixture of FIG. 18 .

FIG. 22 shows a fixture installed on the pillar under the newel-wheelhandrail drive mode.

FIG. 23 is an enlarged view of B of FIG. 18 .

FIG. 24 is an illustration of the fixture of FIG. 22 .

FIG. 25 illustrates a temperature sensor assembly fixed on an escalatorby the fixture of FIG. 22 .

FIG. 26 illustrates a flow chart in accordance with the first embodimentof the monitoring method of the present invention.

FIG. 27 is a block diagram with respect to the escalator monitoringsystem in accordance with the eighth embodiment of the presentinvention.

FIG. 28 illustrates the ratio of the power output per unit time to thepassenger traffic per unit time.

FIG. 29 illustrates a relationship among the real-time monitoring deviceof the present invention, passenger traffic, and motor power.

FIG. 30 illustrates another relationship among the real-time monitoringdevice of the present invention, passenger traffic, and motor power.

FIG. 31 illustrates a flow chart in accordance with the secondembodiment of the monitoring method of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The escalator monitoring system of the present invention preventsfailure of an escalator and provides predictive maintenance thereto, andthus reduces labor and cost associated with the maintenance of theescalator, through a data collection device. Located at or near theparts of the escalator in connection with its safety and normaloperating condition, the data collection device collects data while theescalator is running, analyzes, predicts, and determines the operatingcondition of the escalator, and responses to the determined operatingcondition.

The escalator monitoring system of the invention can monitor, amongothers, vibration of the drive devices or parts of the escalator,lubrication condition of the key parts of the escalator that are incontact with each other, temperature of the key parts of the escalator,and motor power of the escalator with respect to varying passengertraffic. The drive devices or parts of which vibration need to bemonitored include gearboxes and steps. The gearboxes and steps in normaland abnormal operating conditions vibrate differently, and the vibrationdata reflecting the vibration difference can be used to analyze,predict, and determine the operating condition of the escalator, andthus to provide predictive maintenance. The parts between which thelubrication condition need to be monitored include a drive and a drivechain, the drive chain and a drive chain sprockets, and step guide padsand skirt panels. These parts are metal and move relative to each other.Where the lubrication therebetween is sufficient, these metal parts movesmoothly and generates uniform yet soft sound. But when the lubricationruns low or when the parts become rust or dusty, the movement would beless smooth while generating a penetrating noise. Thus, sound datacollected from therebetween can be used to monitor the adequacy of thelubrication, and thus to provide predictive maintenance by analyzing,predicting, and determining the operating condition of the escalator.Temperature gauges may be arranged at the key parts of the escalator tomonitor their temperature changes, which in turn help to analyze,predict, and determine the operating condition of the escalator, andthus to provide predictive maintenance. The key parts include but arenot limited to handrails. The handrails operate normally when theirtemperature changes in a normal range; otherwise, they might bemalfunctioning. Passenger traffic impacts the normal operation of theescalator. High passenger traffic beyond the capacity of the escalatorcan cause a safety issue. And the passenger traffic can be evaluatedbased on the motor power of the escalator. Thus, the motor power can beused to analyze, predict, and determine the operating condition of theescalator, and thus to provide predictive maintenance.

The present invention is further described in detail in the embodimentsbelow.

FIG. 1 is a flow chart with respect to the escalator monitoring systemin accordance with the first embodiment.

In the first embodiment of the present invention, the escalatormonitoring device monitors the lubrication condition of an escalator. Ituses a data collection device to collect data about the sound generatedby the metal parts, to analyze the sound data, and to determine whetherthe lubrication condition is normal. The data collection device isgenerally a digital microphone module. It is positioned near contactposition between a drive and a drive chain, contact position between thedrive chain sprocket and the drive chain of an escalator, or contactposition between the skirt panel and the step guide pad under the stepfront cover, or, if need be, at the position on the truss of anescalator near the contact position between the drive chain and themotor, or any position near parts that are in contact and thus requiremonitoring. The microphone module may use ordinary microphones commonlyused in a cellphone, or ultrasonic microphones. It can record soundwaves and support playback function. The digital microphone modulecontinuously collects sound data and saves sound documents, whichdocuments can replay as needed.

In FIG. 1 , sound data collection device 1 collects sound data at acontact position between a drive and a drive chain, at a contactposition between the drive chain sprocket and the drive chain of thegearbox of an escalator, or at a contact position between the skirtpanel and the step guide pad under the step front cover. A sounddocument containing the sound data is saved at local data processingdevice 2 of local device 6, and processed through band-pass filter 3.The document is then sent via sound data transmittal device 4 to cloudprocessor 5.

At the band-pass filter 3, the sound data is denoised so that sound datawithin certain frequency range [F_(L) to F_(H)] is obtained. Usually,F_(L) is about 1000-5000 Hz, and F_(H) is above 10000 Hz, because sounddata within that range is highly likely relevant to noises produced byfriction between metals. This process can be done with Spectrogramfunction spectrograph.

FIG. 2 is a Spectrogram function spectrograph that exemplarilyillustrates the process of denoising low-frequency and high frequencynoises with the band-pass filter. FIG. 2(a) shows sound signalsgenerated by contact between a normal chain without rust and a chainsprocket, which mainly focus on low-frequency range. FIG. 2(b) showssound signals generated by contact between a rusty chain and a chainsprocket, which mainly are a mix of low-frequency noises andhigh-frequency peaks. FIG. 2(c) shows a sound pattern separated afterapplying the band-pass filter with frequency range of [5000, 15000] andretaining only those relevant to the extent of rust existed on thechain.

The next step is to calculate Key Performance Index (KPI) of thefiltered sound data. The KPI relevant to the noise generated by contactbetween metal components may be root-mean-square (RMS) value of thesound data. In this embodiment, a RMS value is used to calculate thepeak value of the filtered sound data.

The RMS value is sent to the cloud processor 5 via the sound datatransmittal device 4. At the cloud processor 5, a comparison is madebetween the RMS value a predetermined threshold value. If the RMS ishigher, the cloud processor responses by sending alarm signals tocustomer service center, or by direct communicating with maintenancepersonnel to conduct lubrication maintenance. The comparison isexemplarily illustrated in FIG. 3 .

The predetermined threshold value may be obtained by testing undervarious lubrication conditions, or by other kinds of experiments.

FIG. 4 is a flow chart with respect to the escalator monitoring systemin accordance with the second embodiment.

In the second embodiment, data collection device 1 collects sound dataat the contact position between the drive and the drive chain, at thecontact position between the drive chain sprocket and the drive chain ofthe gearbox of an escalator, or at the contact position between theskirt panel and the step guide pad under the step front cover. A sounddocument containing the sound data is saved at local data processingdevice 2 of local device 6. The local data processing device 2calculates the special feature value of the sound data, which is thensent via sound data transmittal device 4 to cloud processor 5.

FIG. 5 shows comparisons between sound data of different failure types.FIG. 5(a) shows a sound data pattern produced when step misalignmentoccurs, FIG. 5(b) shows that produced by rusty drive chains, and FIG.5(c) shows a sound data pattern when an escalator is in normal operatingcondition. Apparently, different types of failure sound data havedifferent patterns and, thus, different special feature values.

In this embodiment, the local data processing device 2 calculates thespecial feature value of sound data. The special feature value may bezero crossing rate, energy, entropy of energy, spectral centroid,spectral spread, spectral entropy, spectral flux, spectral rolloff,MFCC, chroma vector or chroma deviation.

The calculated special feature value then is sent to the cloud processor5 via the data transmittal device 4. At the cloud processor 5,classifier 8 is trained with history or saved sound data of differentfailure types. The classifier 8 employs neural network algorithm andadjusts special feature vectors to better represent special featurevalues of different failure types.

FIG. 6 is an illustration of the analysis flow of the classifier 8. Theclassifier 8 sends the calculated special feature values it received tothree neurons. Each neuron analyzes and calculates for each type offailure and outputs predicted confidence interval for each type offailure and result based on the confidence interval, the resultindicating whether the type of failure is going to happen.

FIG. 7 is a flow chart with respect to the escalator monitoring systemin accordance with the third embodiment.

In the third embodiment, data collection device 1 collects sound data atthe contact position between the drive and the drive chain of anescalator, at the contact position between the drive chain sprocket andthe drive chain of an escalator, or at the contact position between theskirt panel and the step guide pad under the step front cover. A sounddocument containing the sound data is sent via sound data transmittaldevice 4 to cloud processor 5. At the cloud processor 5, the sound datais denoised by bass-pass filter 3 so that sound data within certainfrequency range [F_(L) to F_(H)] is obtained. Usually, F_(L) is about1000-5000 Hz, and F_(H) is above 10000 Hz. Then the cloud processor 5calculates Key Performance Index (KPI) of the filtered sound data. TheKPI relevant to the noise generated by contact between metal componentsmay be root-mean-square (RMS) value of the sound data. In thisembodiment, a RMS value is used to calculate the peak value of thefiltered sound data. The cloud processor 5 then compares the RMS valueagainst a predetermined threshold value. If the RMS is higher, the cloudprocessor responses by sending alarm signals to customer service center,or by direct communicating with maintenance personnel to conductlubrication maintenance. The comparison is exemplarily illustrated inFIG. 3 . The predetermined threshold value may be obtained by testingunder various lubrication conditions, or by other kinds of experiments.

FIG. 8 is a flow chart with respect to the escalator monitoring systemin accordance with the fourth embodiment.

In the fourth embodiment, data collection device 1 is a sound datecollection device, which collects sound data at the contact positionbetween the drive and the drive chain, at the contact position betweenthe drive chain sprocket and the drive chain of the gearbox of anescalator, or at the contact position between the skirt panel and thestep guide pad under the step front cover. A sound document containingthe sound data is sent via sound data transmittal device 4 to cloudprocessor 5. At the cloud processor 5, special features value of thesound data is calculated. Classifier 8 is trained with history or savedsound data of different failure types. The classifier 8 employs neuralnetwork algorithm and adjusts special feature vectors to betterrepresent special feature values of different failure types.

In this embodiment, the cloud processor 5 calculates the special featurevalues of sound data of different types. The special feature value maybe zero crossing rate, energy, entropy of energy, spectral centroid,spectral spread, spectral entropy, spectral flux, spectral rolloff,MFCC, chroma vector or chroma deviation.

FIG. 5 shows comparisons between sound data of different failure types.FIG. 5(a) shows a sound data pattern produced when step misalignmentoccurs, FIG. 5(b) shows that produced by rusty drive chains, and FIG.5(c) shows a sound data pattern when an escalator is in normal operatingcondition. Apparently, different types of failure sound data havedifferent patterns and, thus, different special feature values.

FIG. 6 is an illustration of the analysis flow of the classifier 8. Theclassifier 8 sends the calculated special feature values it received tothree neurons. Each neuron analyzes and calculates for each type offailure and outputs predicted confidence interval for each type offailure and result based on the confidence interval.

The third and fourth embodiments differ mainly from the first and secondembodiments in that data processing at the local device 6 in the firstand second embodiments takes place at the cloud processor 5 in the thirdand fourth embodiments. Nonetheless, this may increase cost associatedwith data transmittal from the local device 6 to the cloud processor 5.

FIG. 9 is a flow chart with respect to the escalator monitoring systemin accordance with the fifth embodiment.

In the fifth embodiment, sound data collection device 1 collects sounddata at the contact position between the drive and the drive chain, atthe contact position between the drive chain sprocket and the drivechain of the gearbox of an escalator, or at the contact position betweenthe skirt panel and the step guide pad under the step front cover. Asound document containing the sound data is denoised by band-pass filter3, so that sound data within certain frequency range [F_(L) to F_(H)] isobtained. Usually, F_(L) is about 1000-5000 Hz, and F_(H) is above 10000Hz. Then local data processing device 2 calculates Key Performance Index(KPI) of the filtered sound data. The KPI relevant to the noisegenerated by contact between metal components may be root-mean-square(RMS) value of the sound data. In this embodiment, a RMS value is usedto calculate the peak value of the filtered sound data. In thisembodiment, the local data processing device 2 further compares the RMSvalue against a predetermined threshold value. The comparison process isexemplarily illustrated in FIG. 3 . If the RMS is higher, the local dataprocessing device 2 sends response 7, the response 7 may be alarmsignals. The alarm signals are sent to customer service center, ordirectly communicated with maintenance personnel to conduct lubricationmaintenance. The local data processing device 2 may also send the RMSvalue to a cloud processor only. The predetermined threshold value maybe obtained by testing under various lubrication conditions, or by otherkinds of experiments.

FIG. 10 is a flow chart with respect to the escalator monitoring systemin accordance with the sixth embodiment.

In the sixth embodiment, sound data collection device 1 collects sounddata at the contact position between the drive and the drive chain, atthe contact position between the drive chain sprocket and the drivechain of the gearbox of an escalator, or at the contact position betweenthe skirt panel and the step guide pad under the step front cover. Localdata processing device 2 at local 6 directly calculates special featurevalue of the sound data. Classifier 8 is then trained with history orsaved sound data of different failure types. The classifier 8 employsneural network algorithm and adjusts special feature vectors to betterrepresent special feature values of different failure types. In thisembodiment, the special feature value may be zero crossing rate, energy,entropy of energy, spectral centroid, spectral spread, spectral entropy,spectral flux, spectral rolloff, MFCC, chroma vector or chromadeviation.

FIG. 5 shows comparisons between sound data of different failure types.FIG. 5(a) shows a sound data pattern produced when step misalignmentoccurs, FIG. 5(b) shows a sound data pattern produced by rusty drivechains, and FIG. 5(c) shows a sound data pattern when an escalator is innormal operating condition. Apparently, different types of failure sounddata have different patterns and, thus, different special featurevalues.

FIG. 6 is an illustration of the analysis flow of the classifier 8. Theclassifier 8 sends the calculated special feature values it received tothree neurons. Each neuron analyzes and calculates for each type offailure and outputs predicted confidence interval for each type offailure and result based on the confidence interval. The local dataprocessing device may respond to the result. The response may be alarmsignals. The alarm signals may be sent to customer service center, or bedirectly communicated with maintenance personnel to conduct lubricationmaintenance. It is also possible to send the result directly to a cloudprocessor.

An escalator monitoring system according to the present invention cancollect data of an escalator and analyze it in any environment, eitherindoor or outdoor or quiet or noisy, independent of subjective humanjudgment. It also predicts possible failure without the need to stop theoperation of the escalator, saves maintenance time and reducesassociated cost, improving its safety and ride comfort.

FIG. 11 is the first embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention. In this embodiment, the sound data collecting device1 comprises a sensor circuit box 11. The sensor circuit box 11 comprisesa top wall 12, a bottom wall 13, and side walls 14. A sound picking hole15 is disposed in the top wall 12, the hole 15 being a through hole. Thesound data collection device 1 further comprises a sound sensor 16disposed within the cavity. The sound sensor 16 rests over the bottomwall 13 of the sensor circuit box by means of support 17. Awater-resistant membrane 18 is arranged at the opening of the soundpicking hole facing the cavity and above the sound sensor 16. Thewater-resistant membrane prevents water or humidity from entering intothe sound data collection device 1, and possesses good soundtransmission capability.

FIG. 12 is a second embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention. It differs from the first embodiment in that thesound sensor 16 directly attaches to the top wall 12 of the sensorcircuit box 11 at a position below the sound picking hole 15 and thewater-resistant membrane 18.

FIG. 13 is a third embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention. It differs from the first embodiment in that thesound data collection device 1 is configured as a double-side-used sounddata collection device. Its sensor circuit box 11 has two through-holetype sound picking holes 15 and 19 in the top 12 and the bottom 13,respectively, and two sound sensors 16 and 20. The sound sensors restover the top wall 12 and the bottom wall 13 by means of supports 17 and21, respectively. Each sound picking hole 15, 19 has a water-resistantmembrane 18, 22 attached thereto at the opening facing the cavity.

FIG. 14 is the fourth embodiment of sound data collection device 1 of anescalator monitoring system used in the various embodiments of thepresent invention. It differs from the second embodiment in that thesound data collection device 1 is configured as a double-side-used sounddata collection device. Its sensor circuit box 11 has two through-holetype sound picking holes 15 and 19 in the top wall 12 and the bottomwall 13, respectively, and two sound sensors 16 and 20. Each soundsensor directly attaches to the top wall 12 and the bottom wall 13. Eachsound picking hole 15, 19 has a water-resistant membrane 18, 22 attachedthereto at the opening facing the cavity. Each water-resistant membrane18, 22 is located between the opening of the sound picking hole 15, 19facing the cavity and the sound sensor that is attached to thecorresponding opening.

With reference to FIGS. 15-17 , a flow chart of the sixth embodimentwith respect to an escalator monitoring system in accordance with thepresent invention is explained in detail. This embodiment relates tomonitoring of temperature of a handrail of an escalator.

FIG. 15 illustrates the block diagram of the escalator monitoring systemaccording to the present invention. The monitoring system 100 comprisestemperature sensor assembly 110, data transmittal unit 4, and a server140. The temperature sensor assembly 110 is used to detect ambienttemperature and the temperature of the handrail of an escalator when itis turned off, in idling condition, or in full operating condition.Aback surface of the handrail is the surface which directly contact infriction with metal of a friction wheel, a bearing and a guide wheel orguide block of the handrail and a curve section. For example, thetemperature sensor assembly 110 includes a handrail temperature sensorused to detect the temperature of the back surface of the handrail andan ambient temperature sensor used to detect the ambient temperature. Ingeneral, when the escalator is turned off, the ambient temperature isthe same as the temperature of the handrail. Thus, the technicalpersonnel who need to know the operating condition of an escalatorconcern more about the temperature of the back surface of the handrailand the ambient temperature when the escalator runs in idling and fulloperating conditions. In this embodiment, the temperature sensorassembly 110 detects the ambient temperature and the temperature of theback surface of the handrail when the escalator is in full operatingcondition. The processor 120 collects the ambient temperature and thetemperature of the back surface of the handrail detected by thetemperature sensor assembly, either in wireless manner (e.g., radiofrequency, Bluetooth, zigbee) or in wired manner, and transmits theambient temperature and the temperature of the back surface of thehandrail to the server 130 at a predetermined frequency (for example,once per one minute).

The server 130 compares the ambient temperature with the handrail backsurface and obtains a temperature difference between the ambienttemperature and the handrail back surface, and a predeterminedtemperature difference threshold is stored in the server, and if thetemperature difference between the ambient temperature and the handrailback surface temperature exceeds the predetermined temperaturedifference threshold, the server will send an alarm signal.

Specifically, the server collects the ambient temperatures and thetemperatures of the back surface of the handrail detected by thetemperature sensor assembly 110 during a pre-determined period of timet1, and compares the ambient temperatures and the temperatures of thehandrail, to obtain a first average value M₁ and a first variance σ₁ ofthe temperature differences of the ambient temperatures and thetemperatures of the back surface of the handrail in the pre-determinedperiod. In addition, the server captures the temperature of the backsurface of the handrail and the ambient temperature detected by thetemperature sensor assembly in another predetermined period t₂ andcompare the ambient temperature and the handrail back surfacetemperature, obtains a second mean value M₂ and a second variance σ₂ ofthe temperature difference between the ambient temperature and thetemperature of the back surface of the handrail in this anotherpredetermined period.

In the next, the server compares the first mean value and the secondmean value, and sends a first alarm signal when a difference between thesecond mean value and the first mean value is k times greater than thefirst variance (M₂>M₁+kσ₁), wherein k, set in the server, is an integergreater than 1.

Furthermore, the server determines relation between the first varianceσ₁ and a predetermined first threshold T₁ and the relation between thesecond variance σ₂ and a predetermined second threshold T₂ and if thefirst variance is smaller than the first threshold and the secondvariance is smaller than the second threshold, that is, σ₁<T₁

, σ₂<T₂, the local data processing device or the cloud processor willsend a second alarm signal. Comparing with the first alarm signal, thesecond alarm signal is more accurate.

FIGS. 16 and 17 illustrate an escalator in frictional-wheel handraildrive mode and newel-wheel handrail drive mode, respectively. In FIG. 16, a friction wheel 101 and the handrail 102 are shown. In FIG. 17 , anewel wheel 103 is shown. In these two different handrail drive modes,handrails move along different directions. Because a position to which afixture for the temperature sensor assembly installs turns on a movingdirection of the handrail, installation of the fixture presentsdifferent situations in the two handrail drive modes, which areintroduced in detail below.

FIGS. 18-21 demonstrate that the fixture 104 is installed on a C-profilecomponent of the skirt panel in the frictional-wheel handrail drivemode. As shown in FIGS. 19-20 , the fixture 104 is installed near thefriction wheel and on the C-profile component of the skirt panel. Thefixture comprises a bracket 41 and an adjustable plate 42. Theadjustable plate 42 is installed on the bracket through connection meanssuch as bolts and nuts. The bracket 41 is installed on the C-profilecomponent of the skirt panel through bolts and nuts. The temperaturesensor assembly 43 is installed on the end of the adjustable plate 42far away from the C-profile component.

One of the adjustable plate 42 and the bracket 41 has a first elongatedhole extending along the length direction, and the other has an opening.Bolts may pass through the first elongated hole and the opening, therebyconnect the adjustable plate and the bracket, and adjust distance Abetween the temperature sensor assembly and the C-profile componentalong the length direction based on the alignment positions of theopening relative to the first elongated hole along the length direction.Thus a handrail center distance L can be changed so as to adapt todifferent types of escalators.

FIGS. 22-25 demonstrate that the fixture 40 is installed on a pillar inthe newel-wheel handrail drive mode. As shown in FIGS. 22-23 , thefixture is installed on the pillar. The fixture 40 comprises a bracket41 and an adjustable plate 42. The adjustable plate 42 is installed onthe bracket through connection means such as bolts and nuts. The bracket41 is installed on the pillar through bolts and nuts. The temperaturesensor assembly 43 is installed on the end of the adjustable plate faraway from the pillar.

One of the adjustable plate and the bracket has a second elongated hole44 extending along the width direction perpendicular to the lengthdirection, and the other has a third elongated hole 45 extending alongthe length direction. Bolts may pass through the second and thirdelongated holes, and thus connect the adjustable plate and the bracket.They also adjust the distance between the temperature sensor assemblyand the pillar along the length direction (handrail center distance L)and the distance between the temperature sensor assembly and thehandrail along the width direction W, based on the alignment positionsof the second and third elongated holes.

FIG. 26 illustrates a flow chart in accordance with the first embodimentof the monitoring method of the present invention. At step 100′, thetemperature sensor assembly is used to detect ambient temperature andthe temperature of the handrail of an escalator when it is turned off,in idling condition, or in full operating condition. At step 200′, adata transferring unit is used to receive the ambient temperature andthe temperature of the back surface of the handrail from the temperaturesensor assembly and transfer the ambient temperature and the handrailback surface temperature to a local data processing device or a cloudprocessor in a predetermined frequency. At step 300′, the servercompares the ambient temperature with the handrail back surface andobtains the temperature difference between the ambient temperature andthe handrail back surface, and a predetermined temperature differencethreshold is stored in the local data processing device or the cloudprocessor. If the temperature difference between the ambient temperatureand the temperature of the back surface of the handrail back surfaceexceeds the predetermined temperature difference threshold, the serverwill send an alarm signal.

At step 300′, the server further captures the temperature of the backsurface of the handrail and the ambient temperature detected by thetemperature sensor assembly in a predetermined period and compare theambient temperature and the temperature of the back surface of thehandrail, obtains a first mean value and a first variance of thetemperature difference between the ambient temperature and thetemperature of the back surface of the handrail in the predeterminedperiod.

At the step 300′, the server further captures the temperature of theback surface of the handrail and the ambient temperature detected by thetemperature sensor assembly in another predetermined period and comparesthe ambient temperature and the temperature of the back surface of thehandrail, and obtains a second mean value and a second variance of thetemperature difference between the ambient temperature and thetemperature of the back surface of the handrail in this anotherpredetermined period.

At the step 300′, the server is configured that the server sends a firstalarm signal when the difference between the second mean value and thefirst mean value is k times greater than the first variance, wherein k,set in the server, is an integer greater than 1.

At the step 300′, the server is further configured the server determinesrelation between the first variance and a predetermined first thresholdand relation between the second variance and a predetermined secondthreshold, and if the first variance is smaller than the first thresholdand the second variance is smaller than the second threshold, the serverwill send a second alarm signal.

The escalator monitoring system allows real-time monitoring of theoperating condition of the handrails. Possible issues can be known inadvance, and thus a safer escalator is provided. Furthermore, thereal-time monitoring device applies to different installationcircumstances of an escalator, and the fixture for the temperaturesensor assembly is adjustable. The installation position of the fixturecan be adjusted in light of different installation dimensions of varioustypes of escalators.

With respect to FIGS. 27-31 , a flow chart with respect to the eighthembodiment of the escalator monitoring system in accordance with thepresent invention is described in detail. The embodiment relates tomonitoring of passenger traffic of the escalator monitoring inaccordance with the present invention.

FIG. 27 is a block diagram of the escalator monitoring system inaccordance with the present invention. The monitoring system 100comprises passenger traffic sensor 211, motor power sensor 212, datatransmittal unit 4, and server 130. The passenger traffic sensor 211 ismeans installed at the entrance of an escalator for emitting light beamsin opposing directions, and the frequency of the light beams beinginterrupted reflects the passenger traffic entering onto the escalator.The motor power sensor 212 is so configured as to detect motor powerconsumption per unit time. The data transmittal unit 4 collects thepassenger traffic and the motor power consumption per unit time, andtransmits them to the server 130 by ways such as 2G, 3G, or 4G. Theserver 130 calculates the ratio (KPI) of the passenger traffic per unittime to the motor power consumption per unit time, and compares theratio against a pre-determined ratio stored in the server 130. If theratio KPI shows abnormal changes as compared with the pre-determinedratio, the server predicts the time when the predicted failure willoccur and sends out predictive maintenance signals. For example, thenumeral “2” shown in FIG. 28 illustrates that the ratio of the passengertraffic per unit time to the motor power per unit time increasessuddenly, which indicates that the passenger traffic at that time is notcomparable to the motor power consumption and that the escalator ismalfunctioning. Persons in the art will appreciate that the passengertraffic per unit time is a ratio of whole passenger traffic in a periodof time and the time, and the whole passenger traffic in the time shouldnot be too small.

FIGS. 29 and 30 show two incomparable relationships between thepassenger traffic per unit time and the motor power consumption per unittime, respectively. FIG. 29 illustrates that if the passenger trafficsensor detects no traffic yet the motor power consumption detected bythe power sensor is incomparable to a predetermined power consumptionunder the situation that there is no passenger traffic, the escalator isconsidered as malfunctioning and the server sends out an alarm signal.FIG. 30 illustrates that if the passenger traffic sensor detects atleast some traffic yet the motor power consumption is operating underidling condition during a period of time beyond a pre-determinedthreshold value, the escalator may suddenly speed up, causing passengerfalling down and suffering an injury, and the server sends out alarmsignals.

In addition, when there is no passenger traffic, the power sensordetects a mean power consumption in a predetermined period of time. Apredetermined threshold range is stored in the server. If the mean powerconsumption falls into the predetermined threshold range stored in theserver, the server sends out an alarm signal.

FIG. 31 illustrates a flow chart of the second embodiment of themonitoring method of an escalator in accordance with the presentinvention.

At step 2100, a passenger traffic sensor is used to detect the passengertraffic entering onto an escalator per unit time. At step 2200, a powersensor is used to detect the motor power consumption per unit time. Atstep 2300, a data transferring unit is used to transfer the peopletraffic detected in a unit time and the power consumption detected in aunit time to a server. At step 2400, the server determines the runningcondition of the escalator according to relation between the peopletraffic in a unit time and the power consumption in a unit time todetermine whether or not an alarm signal needs to be sent.

In addition, at step 2400, the server further calculates a ratio betweenthe power consumption in a unit time and the people traffic in a unittime and if the ratio abnormally changes, the server will send an alarmsignal and if the ratio does not abnormally changes but has a tendencyto increase in a predetermined time and goes beyond a predeterminedthreshold, the server will send an alarm signal.

In addition, in the case that the escalator is in a standby runningcondition, at the step 2200, the power sensor detects an average powerin a predetermined period, and at the step 2400, if the average powerdoes not fall in a predetermined threshold range stored in the server,the server will send an alarm signal.

At step 2400, the server is further configured that in a period beyond apredetermined threshold period, if the people traffic sensor detectsthat the people traffic is 0 and the power sensor detects that the powerconsumption is not comparable to a set power consumption when the peopletraffic is 0, the server will send an alarm signal.

At step 2400, the server is further configured that in a period beyond apredetermined threshold period, if the people traffic sensor detectsthat the people traffic is not 0 and the power sensor detects that thepower consumption is a standby power output, the server will send analarm signal.

The real-time monitoring device according to the present inventionallows real-time monitoring of the relationship between passengertraffic and power output and is informed of the operating condition ofan escalator, thus improving the maintenance of the escalator,ameliorating its energy consumption, and making it safer.

The server mentioned in the specification may be a local server alongwith the data collection device, or a cloud processor capable of remotemonitoring. All data may be processed at the local processor only, or atthe cloud processor only, or, as explained in an embodiment above, atthe local processor for some of work and then at the cloud processor forthe rest. The present invention incorporates herein various embodimentsbased on these conceptions and is not limited to those embodimentsdescribed above.

It is intended that the abovementioned embodiments are exemplary onlyand shall not be considered as limitations of the present invention. Thefeatures in various embodiments may be combined to implement moreembodiments of the invention, of which the scope is only determined bythe appended claims. A variety of modifications and alterations may bemade to the described embodiments without departing the scope of theinvention.

What is claimed is:
 1. An escalator monitoring system, comprising: adata collection device disposed near parts of an escalator that need tobe monitored for collecting data of the parts; a data transmittal deviceused to transmit data relevant to safe operation of the escalator; alocal data processing device or cloud processors used to receive thedata relevant to the safe operation of the escalator, to compare itagainst a threshold value stored therein and derived from the parts innormal operating conditions, and to respond to comparison result,wherein the data collection device is a people traffic sensor fordetecting the people traffic entering the escalator in a unit time, andan power sensor for detecting the power consumption in a unit time, adata transferring unit transfers the people traffic detected in a unittime and the power consumption detected in a unit time to the local dataprocessing device or the cloud processors, the local data processingdevice or the cloud processors determines the running condition of theescalator according to the relation between the people traffic in a unittime and the power consumption in a unit time to determine whether ornot an alarm signal needs to be sent, and wherein the local processingdevice or the cloud processors calculates a ratio between the powerconsumption in a unit time and the people traffic in a unit time and ifthe ratio changes abnormally, indicating that the passenger traffic atthat time is not comparable to the motor power consumption and that theescalator is malfunctioning, the local processing device or the cloudprocessors will send an alarm signal and if the ratio does not changeabnormally but has a tendency to increase in a predetermined time and isgreater than a predetermined threshold, the local processing device orthe cloud processors will send an alarm signal.
 2. The escalatormonitoring system according to claim 1, wherein the escalator is in astandby running condition, the power sensor detects an average power ina predetermined period and if the average power is not in apredetermined threshold range stored in the local data processing deviceor the cloud processors, the local data processing device or the cloudprocessors will send an alarm signal.
 3. The escalator monitoring systemaccording to claim 1, wherein the local data processing device or thecloud processors is further configured that in a period beyond apredetermined threshold period, if the people traffic sensor detectsthat the people traffic is 0 and the power sensor detects that the powerconsumption is not matched to a set power consumption when the peopletraffic is 0, the local data processing device or the cloud processorswill send an alarm signal.
 4. The escalator monitoring system accordingto claim 1, wherein the local data processing device or the cloudprocessors is further configured that in a period beyond a predeterminedthreshold period, if the people traffic sensor detects that the peopletraffic is not 0 and the power sensor detects that the power consumptionis a standby power output, the local data processing device or the cloudprocessors will send an alarm signal.
 5. A monitoring method for anescalator, comprising: using a people traffic sensor to detect peopletraffic entering into the escalator in a unit time; using an powersensor to detect the power consumption in a unit time; using a datatransferring unit to transfer the people traffic detected in a unit timeand the power consumption detected in a unit time to a local dataprocessing device and a cloud processors, wherein a server determinesthe running condition of the escalator according to the relation betweenthe people traffic in a unit time and the power consumption in a unittime to determine whether or not an alarm signal needs to be sent, andwherein the local data processing device or the cloud processorscalculates a ratio between the power consumption in a unit time and thepeople traffic in a unit time and if the ratio changes abnormally,indicating that the passenger traffic at that time is not comparable tothe motor power consumption and that the escalator is malfunctioning,the local data processing device or the cloud processors will send analarm signal and if the ratio does not change abnormally but has atendency to increase in a predetermined time and is greater than apredetermined threshold, the local data processing device or the cloudprocessors will send an alarm signal.
 6. The method according to claim5, wherein in the case that the escalator is in a standby runningcondition, the power sensor detects an average power in a predeterminedperiod and if the average power is not in a predetermined thresholdrange stored in the local data processing device or the cloudprocessors, the local data processing device or the cloud processorswill send an alarm signal.
 7. The method according to claim 6, whereinthe local data processing device or the cloud processors is furtherconfigured that in a period beyond a predetermined threshold period, ifthe people traffic sensor detects that the people traffic is 0 and thepower sensor detects that the power consumption is not matched to a setpower consumption when the people traffic is 0, the local dataprocessing device or the cloud processors will send an alarm signal. 8.The method according to claim 6, wherein the local data processingdevice or the cloud processors is further configured that in a periodbeyond a predetermined threshold period, if the people traffic sensordetects that the people traffic is not 0 and the power sensor detectsthat the power consumption is a standby power output, the local dataprocessing device or the cloud processors will send an alarm signal.